Thunder compiler optimizes PyTorch models, achieving 40% faster processing time for large language models with 7-billion parameters.
Thunder seamlessly integrates with PyTorch's optimization tools, extending speed improvements to multi-GPU and distributed training frameworks like DDP and FSDP.
Thunder's user-friendly design and Thunder.Jit() function make it easy for developers to enhance PyTorch model performance with minimal adjustments.
Thunder enables team leads to supercharge their development lifecycle by streamlining and accelerating the training process of deep learning models within the PyTorch ecosystem.
#thunder-compiler #pytorch-ecosystem #large-language-models #multi-gpu-training #user-friendly-design
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